Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
Shinnosuke Matsuo (Kyushu University); Seiichi Uchida (Kyushu University); Ryoma Bise (Kyushu University); Daiki Suehiro (Kyushu University)
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07 Jun 2023
This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags.
We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large.
We demonstrate the effectiveness of the proposed method using some benchmark datasets.
